SVEHDRI

Implementation of our papers entitiled Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot published in TCSVT and Restoration of HDR Images for SVE-Based HDRI via a Novel DCNN published in ICME. This code is mainly based on BasicSR. Thanks for their provided codes.

Abstract

Spatially varying exposure (SVE) is a promising choice for high-dynamic-range (HDR) imaging (HDRI). The SVE-based HDRI, which is called single-shot HDRI, is an efficient solution to avoid ghosting artifacts. However, it is very challenging to restore a full-resolution HDR image from a real-world image with SVE because: a) only one-third of pixels with varying exposures are captured by camera in a Bayer pattern, b) some of the captured pixels are over- and under-exposed. For the former challenge, a spatially varying convolution (SVC) is designed to process the Bayer images carried with varying exposures. For the latter one, an exposure-guidance method is proposed against the interference from over- and under-exposed pixels. Finally, a joint demosaicing and HDRI deep learning framework is formalized to include the two novel components and to realize an end-to-end single-shot HDRI. Experiments indicate that the proposed end-to-end framework avoids the problem of cumulative errors and surpasses the related state-of-the-art methods.

Code

For training and test, replace the network model in BasicSR with our provided Model.py and Loss.py, then run:

python train.py -opt train_options.yml
python test.py -opt test_options.yml

Note that you also need to modify the original datas/LRHR_datasets.py and models/SR_model.py to read our datasets.

A New Dataset Containing the Original Files

In addition, we open source our own datasets. The dataset contains 177 sets of static scene exposure sequences taken by Cannon5D4 camera, each sequence contains the results of 7 shots, with an exposure interval of 1eV and ISO of 800. In order to advance the research in the field of raw images, all the original files after shooting are preserved.Specifically, it contains CR2 files, JPEG files, and detailed shooting parameters contained in the file ownership information. The training set, validation set and test set for this task are obtained by sampling the original files.

The download address and preprocessing code for the dataset can be downloaded from the following link Download(code: un57)

It is worth noting that this dataset is not limited to single-shot HDRI tasks.

Citation

If you find this code or dataset is helpful in your research, please cite:

@ARTICLE{9622212,
  author={Xu, Yilun and Liu, Ziyang and Wu, Xingming and Chen, Weihai and Wen, Changyun and Li, Zhengguo},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Deep Joint Demosaicing and High Dynamic Range Imaging within a Single Shot}, 
  year={2021},
  volume={},
  number={},
  pages={},
  doi={10.1109/TCSVT.2021.3129691}
}

@INPROCEEDINGS{9428198,
  author={Xu, Yilun and Liu, Ziyang and Wu, Xingming and Chen, Weihai and Li, Zhengguo},
  booktitle={2021 IEEE International Conference on Multimedia and Expo (ICME)}, 
  title={Restoration of HDR Images for SVE-Based HDRI via a Novel DCNN}, 
  year={2021},
  pages={1-6},
  doi={10.1109/ICME51207.2021.9428198}
}

Contact

If you have any questions, feel free to E-mail me via: yilunxu_buaa@163.com